The strength of Pacific Walker circulation (PWC) significantly affects the global weather patterns, the distribution of mean precipitation, and modulates the rate of global warming. Different indices have been used to assess the PWC strength. Evaluated on different datasets for various study periods, the indices show large discrepancies between the reported trends. In this study, we performed sensitivity analysis of 10 PWC indices and compared them over the 1951-2020 period using the ERA5 reanalyses. The time series of normalised indices generally agree on the annual-mean PWC strength. The highest correlations (exceeding r=0.9) are between the indices that describe closely linked physical processes. The trends of PWC strength are strongly affected by the choice of representative time period. For the commonly used 1981-2010 period, the trends show strengthening of the PWC. However, trends computed for longer period (i.e. 1951-2020) are mostly neutral, whereas the past two decades (2000-2020) display weakening of the PWC, although it is statistically not significant. The temporal evolution of trends suggests multidecadal variability of PWC strength with a period of about 35 years, implying a continued weakening of the PWC in the next decade.

Resource allocation drives the above-ground distribution of mass in grass plants across discrete developmental units called phytomers. Although the number of phytomers varies in genetically-identical grasses, there frequently isn't an associated variance in some summary phenotypes. To understand what may be driving this, we tracked the growth of 30 S. italica plants from genotypes B100 and A10.1. We experimentally observed that plants from the genotype B100 had between 20 and 22 phytomers, while plants from the genotype A10.1 had between 7 and 9 phytomers. B100 plants with more phytomers (e.g., 22) did not grow taller or have more total leaf length, despite having more leaves than plants with fewer phytomers (e.g., 20). A10.1 plants with more phytomers (e.g., 9) did grow taller and had more total leaf length than those with fewer phytomers (e.g., 7). We developed a dynamical model to determine if these patterns are emergent from the underlying growth structure. The model is parameterized using the number of phytomers and related developmental time parameters: leaf emergence, stem and leaf elongation time, panicle emergence, and flowering time. The model uses the semi-sequential nature of phytomer growth as its structure. The model predicts that differences in timing of the shift to reproductive growth could explain the patterns observed. Experimental measurements suggest this shift is primarily due to tuning the developmental time parameter controlling the units contributing to the stem, rather than leaves.

Forest ecosystems are the largest terrestrial carbon sink and monitoring them effectively, particularly in the context of global change, requires rapid and accurate determination of tree functional traits that indicate forest health. Hyperspectral reflectance has the capacity to predict leaf traits non-destructively using multivariate statistical approaches. The ability of hyperspectral data to estimate tree physiochemical responses is affected by wavelength range selection and the influence of wavelength on accuracy of trait estimation is not well known, especially for more complex physiological processes. To bridge this knowledge gap, this study examined chemical and physiological responses of one-year-old black walnut (Juglans nigra L.) and northern red oak (Quercus rubra L.) to a combination of biotic and abiotic stress events, including pathogen inoculation, water stress, nutrient deficiency, and salt deposition. Leaf photosynthetic-related, water-related, and chemical traits were paired with hyperspectral measurements spanning 350−2500 nm. A total of 100 different wavelength ranges were evaluated using PLSR to determine the variation prediction accuracy. Key findings indicated that incorporating short infrared wavelength ranges (1300−2500 nm) significantly enhanced trait prediction accuracy. In addition, this study also demonstrated that hyperspectral data can detect tree stress responses at fine-scale chemical and physiological levels that are in agreement with reference measurement responses to the different stressors. We suggest that hyperspectral reflectance can potentially offer a solution for monitoring forest health in multi-stress environments with an increase in the accuracy of trait estimations and an expansion of the classes of traits estimated.

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. Early in the COVID-19 epidemic scientists and engineers demonstrated the use of wastewater as a medium by which the virus could be monitored both temporally and spatially. In the United Kingdom this evidence prompted the development of National wastewater surveillance programmes involving UK Government agencies academics and private companies. In terms of speed and scale the programmes have proven to be unique in its efforts to deliver measures of virus dynamics across a large proportion of the populations in all four regions of the country. This success has demonstrated that wastewater-based epidemiology (WBE) can be a critical component in public health protection at regional and national levels and looking beyond COVID-19 is likely to be a core tool in monitoring and informing on a range of biological and chemical markers of human health; some established (e.g. pharmaceutical usage) and some emerging (e.g. metabolites of stress). We present here a discussion of uncertainty and variation associated with surveillance of wastewater focusing on lessons-learned from the UK programmes monitoring COVID-19 but addressing the areas that can broadly be applied to WBE more generally. Through discussion and the use of case studies we highlight that sources of uncertainty and variability that can impact measurement quality and importantly interpretation of data for public health decision-making are varied and complex. While some factors remain poorly understood and require dedicated research we present approaches taken by the UK programmes to manage and mitigate the more tractable components. This work provides a platform to integrate uncertainty management through data analysis quality assurance and modelling into the inevitable expansion of WBE activities as part of One Health initiatives.

Continuous measurement and monitoring of river or creek surface water coverage is crucial for studying the exchange fluxes between the surface and subsurface water. These fluxes directly impact carbon and nitrogen exchange and cycles, which are related to organic matter transport and reactions. While satellite and related techniques have been widely used for large-scale monitoring, they may not be accurate, sensitive, or cost-efficient for monitoring and tracking of surface water at fine-scale spatial (i.e., sub-meter) and temporal (i.e., daily) variations. This is especially true for small creeks with large plant canopy coverage. On-site in-situ sensors monitoring methods primarily yield point data, often insufficient in capturing the entire spatial distribution. Wildlife cameras have proven a cost-efficient way to continuously monitor surface water coverage of rivers and creeks. To efficiently analyze the images and/or videos from the wildlife cameras, in this study, two machine learning approaches, YOLOv8 and Mask2Former, have been applied. Both models were trained by images obtained from the public dataset ADE20k along with a small dataset from wildlife camera photos collected at the current study area. Once surface water coverage is segmented, the width of the surface water in real world can be approximated according to the wildlife camera, lens, and positioning parameters. In this study, surface water was detected and monitored by applying the proposed approaches for the six wildlife cameras in the Yakima River Basin in 2023 to 2024 in United States of America. Though Mask2Former model provides slightly better transferability, both models can accurately capture the surface water from the wildlife cameras, which are installed in significantly different environments, such as the different brightness, contrast, and varying front scene object blockages. The proposed approach enables long-term continuous monitoring and quantification of river intermittency and water availability with high accuracy and low-cost, which will benefit river ecosystem research and management.

A substantial amount of the tropical forests of South America and Africa is generated through moisture recycling (i.e., forest rainfall self-reliance). Thus, deforestation that reduces evaporation and dampens the water cycle can further increase the risk of water-stress-induced forest loss in downwind areas, particularly during water scarce periods. However, few studies have investigated dry period forest rainfall self-reliance over longer records and consistently compared the rainforest moisture recycling in both continents. Here, we analyze dry-season anomalies of moisture recycling for mean-years and dry-years, in the South American (Amazon) and African (Congo) rainforests over the years 1980-2013. We find that, in the dry seasons, the reliance of forest rainfall on their own moisture supply (ρfor) increases by 7% (from a mean annual value of 26% to 28%) in the Amazon and up to 30% (from 28% to 36%) in the Congo. Dry years further amplify dry season ρfor in both regions by 4-5%. In both the Amazon and Congo, dry season amplification of ρfor is strongest in regions with a high mean annual ρfor. In the Amazon, forest rainfall self-reliance has declined over time. At the country scale, dry season ρfor can differ drastically from mean annual ρfor. In for example Bolivia and Gabon, mean annual ρfor is ~30% while dry season ρfor is ~50%. The dry period amplification of forest rainfall self-reliance further highlights the role of forests for sustaining their own resilience, and for maintaining downwind rainfall at both regional and national scales.

We compared snowfall, and snow water equivalent (SWE) accumulation and ablation simulations from the WRF-Hydro model with the U.S. National Water Model (NWM) configuration against observations at a set of representative point locations from Snow Telemetry (SNOTEL) sites across the western U.S. We focused on the model’s partitioning of precipitation between rain and snow and selected sites that span the variability of the percentage of rain on snow precipitation events. Our results show that the NWM generally under-estimates SWE and tends to melt snow earlier than observations in part due to errors in the precipitation and air temperature inputs. We reduced some of the observed and modeled discrepancies by using SNOTEL snow-adjusted precipitation and removing air temperature biases, based on observations. These input changes produced an average 59% improvement in the peak SWE. Modeled peak SWE was further improved using humidity-dependent rain-snow-separation. Both dew point and wet-bulb parameterizations were evaluated, with the dew-point parameterization giving better overall improvement, reducing the bias in SWE by 18% compared to the NWM air temperature-based scheme. This modification also improved melt timing with the number of site years having difference between modeled and observed date of half melt from peak SWE six or more days reduced by 6%. These SWE magnitude and timing improvements varied when analyzed for each rain-on-snow percentage class, with generally better results at sites where most precipitation events fall either as snow or as rain, and less improvement when there is a mix of snow and rain-on-snow events.

Most studies on the impacts of extreme hydrometeorological events on hydrological processes have focused primarily on surface water systems rather than groundwater systems. This study explores and seeks to untangle the complex nature of groundwater dynamics and resilience across British Columbia (BC) in response to the 2021 heatwave-intensified drought and atmospheric rivers (ARs). Historically, there have been many episodic drought events along with substantially wet periods. However, 2021 marked an unprecedented year for the immediate co-occurrence of intense and extreme drought and deluge. This weather whiplash resulted in the lowest and highest groundwater levels on record for many wells across BC. The record meteorological drought, intensified by a 13-day heatwave in late June, affected the entire province and lasted for over 50 days in the south coast region. This was followed in November by the most intense ARs to make landfall on record in southwestern BC. Groundwater hydrograph anomalies for 2021 were computed relative to their short-term historical mean for 194 provincial observation wells across the province. The 2021 anomalies showed a limited but distinct range of responses to both the drought and ARs, and cluster into three response groups, largely associated with their respective hydroclimatic regime. Many coastal wells showed a strong response to drought; however, nearly all wells in the southern interior responded substantially, with groundwater levels significantly below their historical range by late summer. Presently, groundwater levels seem to have recovered across the province, especially on the coast. This resiliency is attributed in part to the ARs that made landfall since last year along with a particularly wet, La Niña winter. The majority of coastal wells showed a much stronger signal to the ARs compared to the interior or eastern BC wells, likely due to the more rapid and intense rainfall experienced in southwestern BC. Groundwater systems across BC were variably impacted by these hydrometeorological extremes, showcasing the need for focused and area-specific approaches to water allocation decisions in assuring sustainable withdrawal practices.

NOAA’s Daily Optimum Interpolation Sea Surface Temperature (DOISST) indicates that globally averaged sea surface temperature (SST) broke record in March 2023 and set new record highs in April, July, and August 2023. This has raised intense media interest and public concern about causes and connections to climate change. Our analysis indicates that the record high SSTs qualified as marine heatwaves (MHWs) and even super-MHWs as defined in this study, and are attributed to three factors: (i) a linear trend, (ii) a shift to the warm phase of the multi-decadal Pacific-Atlantic-Arctic Oscillation (PAO) pattern which is identified in this study, and (iii) the transition from the triple-dip succession of La Niña events to the 2023 El Niño event. One-Sentence Summary The extreme warm SSTs in 2023 resulted from linear warming trends, a pattern of low-frequency oscillation, and the El Niño event.

Addressing the need for convergence of different sources of knowledge to deal with complex issues such as global change, this paper presents the results of collaboration between artists and scientists to study social-ecological-technological systems (SETS). We focus on informal roads as an example of SETS. In the absence of public roads local, mostly indigenous communities and others use these forestry roads, seismic line clearings and oil and gas service roads for mobility in Siberian taiga affected by extractive industry. In 2020, with COVID-19, we had to increase our emphasis on virtual forms of data gathering, interpretation, and representations of the results. Presented in this paper forms of transmedia storytelling are designed to allow audience and users as well as the local and indigenous communities to get familiar with the research results, give feedback, and provide their own perspectives, interrelations and interdependencies between different SETS components.

In this study we introduce a pair of idealized tracers to quantify how changes in physical advection and mixing under climate change affect the nutrient supply, new production, and particulate export rates. The low cost and simplicity of these tracers allows us to explore the sensitivity of the model biogeochemistry, and in particular its response to a changing physical environment, to the choice of model parameters. Using CESM2.1 with active ocean and ice only, at nominal one-degree resolution, under initial conditions and forcing representative of 2000 and 2100, our idealized nutrient and particulate are within the spread of nitrate and export from CMIP5 models. The simple form of the tracers allows us to identify the physical controls on the changing rates of supply, production, and export throughout the year, which together form the different seasonal cycles. We find that the ocean basins with the largest changes in the seasonal cycle over the 21st century are the North Atlantic, the Arctic, and the eastern tropical Pacific. We present results comparing the controls across basins, focusing on shifts in the timing of deepening mixed layers and maximum production rate in the northern North Atlantic through the Arctic, and changes in the spatial and temporal patterns of vertical advective exchange in the tropics and subtropics of the Pacific and Indian Oceans. In both cases we discuss how much these changes depend on the biogeochemical model parameter values.

Realistically approximating the basal melting of ice shelves is critical for reliable climate model projections and the process representations in ice-ocean interaction. In this regard, extensive research attributes the massive thinning of vulnerable ice shelves to basal melting enhancement driven by ocean water warming, focusing mainly on oceanic warm water intrusion into the sub-shelf basins. However, climate models mainly underestimated the impacts of probable small-scale processes at the ice-ocean interface on basal melting by using smooth ice base topographies. This paper provides new insights into how small-scale features on the ice-ocean interface contribute to basal melting enhancement and spatial distribution. We developed a time-dependent, two-dimensional ice-shelf plume model as an optimal tool that allows a high-resolution representation of basal topography and with the unique ability to provide valuable information from the mixed boundary layer between ocean and ice shelves. In an exemplary case study for the floating ice tongue of the 79◦ North Glacier, systematic sensitive analyses were performed with the developed model. Our results show that the sub-km-scale basal channels with realistic dimensions increase the mean basal melt rate and generate extreme and sizeable lateral variability of melting at the grounding line. This mechanism is not reproducible with the tuning of drag coefficient. Besides, it reveals that the subglacial discharge in the channels has contradicting effects of reducing the melt rate by refreshing the sea water and increasing the freezing point while increasing the melt rate due to high water speed. However, the latter was dominant in our experiments.

A key consideration for evaluating climate projections is uncertainty in radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare those observations with specified climate scenarios, it remains less obvious on how to connect regional climate patterns with these scenarios in real time. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use an attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. The neural network learns to identify “fingerprint” patterns that associate signals of climate change with the scenarios. We illustrate this using output from additional mitigation experiments and highlight regions that are critical for associating the new simulations with likely radiative forcing scenarios.

Increasing anthropogenic CO2 emissions to the atmosphere are partially sequestered into the global oceans through the air-sea exchange of CO2 and its subsequent movement to depth, and this collective large-scale absorption is commonly referred to as the global ocean carbon sink. Quantifying this ocean carbon sink provides a key component for closing the global carbon budget which is used to inform and guide policy decisions. These estimates are typically accompanied by an uncertainty budget built by selecting what are perceived as critical uncertainty components based on selective experimentation. However, there is a growing realisation that these budgets are incomplete and may be underestimated, which limits their power as a constraint within global budgets. In this study, we present a methodology for quantifying spatially and temporally varying uncertainties in the air-sea CO2 flux calculations and data that allows an exhaustive assessment of all known sources of uncertainties, including decorrelation length scales between gridded measurements, and the approach follows standard uncertainty propagation methodologies. The resulting standard uncertainties are higher than previously suggested budgets, but the components are consistent with previous work, and they identify how the significance and importance of key uncertainty components change in space and time. For an exemplar method (the UEP-FNN-U method) the work identifies that we can currently estimate the annual ocean carbon sink to an accuracy of ±0.72PgCyr-1 (1 standard deviation uncertainty). Due to this method having been built on established uncertainty propagation and approaches, it appears applicable to all data-product assessments of the ocean carbon sink.

A document by Sierra Young. Click on the document to view its contents.

The mixing and mingling of magmas of different compositions are important geological processes. They produce various distinctive textures and geochemical signals in both plutonic and volcanic rocks and have implications for eruption triggering. Both processes are widely studied, with prior work focusing on field and textural observations, geochemical analysis of samples, theoretical and numerical modelling, and experiments. However, despite the vast amount of existing literature, there remain numerous unresolved questions. In particular, how does the presence of crystals and exsolved volatiles control the dynamics of mixing and mingling? Furthermore, to what extent can this dependence be parameterised through the effect of crystallinity and vesicularity on bulk magma properties such as viscosity and density? In this contribution, we review the state of the art for models of mixing and mingling processes and how they have been informed by field, analytical, experimental and numerical investigations. We then show how analytical observations of mixed and mingled lavas from four volcanoes (Chaos Crags, Lassen Peak, Mt. Unzen and Soufrière Hills) have been used to infer a conceptual model for mixing and mingling dynamics in magma storage regions. Finally, we review recent advances in incorporating multi-phase effects in numerical modelling of mixing and mingling, and highlight the challenges associated with bringing together empirical conceptual models and theoretically-based numerical simulations.

Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). Although solar flare-driven HF absorption is a well-known impact of SWF, the occurrence of a frequency shift on radio wave signal traversing the lower ionosphere in the early stages of SWF, also known as “Doppler Flash”, is newly reported and not well understood. Some prior investigations have suggested two possible sources that might contribute to the manifestation of Doppler Flash: first, enhancements of plasma density in the D and lower E regions; second, the lowering of the reflection point in the F region. Observations and modeling evidence regarding the manifestation and evolution of Doppler Flash in the ionosphere are limited. This study seeks to advance our understanding of the initial impacts of solar flare-driven SWF. We use WACCM-X to estimate flare-driven enhanced ionization in D, E, and F-regions and a ray-tracing code (Pharlap) to simulate a 1-hop HF communication through the modified ionosphere. Once the ray traveling path has been identified, the model estimates the Doppler frequency shift along the ray path. Finally, the outputs are validated against observations of SWF made with SuperDARN HF radars. We find that changes in refractive index in the D and lower E regions due to plasma density enhancement are the primary cause of Doppler Flash.

Jinjeong Lee, Su Jung Ra, Sung Mi Kim, Eunhee Kim, Myeong-Jin Kang,